To sell or not to sell: Trading your reserved instances in amazon EC2 marketplace
Document Type
Conference Proceeding
Publication Date
7-19-2018
Abstract
Recently, Amazon EC2 offers a reserved instance marketplace, where cloud users can sell their idle reserved instances varying in contract lengths and pricing options for avoiding the waste of their unused reservations. However, without knowing the future demands, it is hard for users to determine how to sell instances optimally, for it would incur more cost if new demands arrive after selling their reserved instances. For dealing with this problem, in this paper we first propose three online selling algorithms to guide cloud users in making decisions whether or not to sell their reservations in Amazon EC2 marketplace while guaranteeing competitive ratios. We prove theoretically that the three proposed online algorithms can guarantee bounded competitive ratios, whose values are specific to the type of reserved instances under consideration. Specifically, for all standard instances (Linux, US East) for 1-year terms in Amazon EC2, compared with a benchmark optimal offline algorithm, our algorithm A3T/4 can achieve a ratio of 2-α-a/4 in managing instance purchasing cost, where α is the entitled discount due to reservation and a is the selling discount specified by the user who sells its reservations. Finally, through extensive experiments based on workload data collected from real-world applications, we validate the effectiveness of our online instance selling algorithms by showing that it can bring significant cost savings to cloud users compared with always keeping their reservations in Amazon EC2 reserved instance marketplace.
Publication Source (Journal or Book title)
Proceedings - International Conference on Distributed Computing Systems
First Page
939
Last Page
948
Recommended Citation
Yang, S., Pan, L., Wang, Q., & Liu, S. (2018). To sell or not to sell: Trading your reserved instances in amazon EC2 marketplace. Proceedings - International Conference on Distributed Computing Systems, 2018-July, 939-948. https://doi.org/10.1109/ICDCS.2018.00095
